SIDE : Self Driving Drones Embrace Uncertainty

Novella Bartolini, Andrea Coletta, Gaia Maselli

Abstract: Aerial drones are increasingly used to perform monitoring tasks in a large number of applications. Current solutions to trajectory planning rely on perfect knowledge of ongoing events requiring inspection. Nevertheless, in many scenarios the events’ time and position can only be estimated with some <i>uncertainty</i> . Unlike previous work, we consider critical scenarios where a squad of drones is required to autonomously inspect an area of interest under <i>uncertainty</i> of time and location of target events. The main goal of the squad is to ensure maximum coverage of event monitoring with minimum average inspection delay. With no initial knowledge, the drones share their local observations of the environment and apply the Parzen-Rosenblatt approach to manage a dynamic probabilistic map of ongoing events. This map is integrated into a virtual force approach for a joint solution to distributed dynamic trajectory planning and collision avoidance. Through extensive simulations and real-field experiments, we compare our proposal against <i>AC-GAP</i> , a state-of-art solution for UAVs, and <i>Sweep</i> , a sweep-based algorithm for multiple robots. We show that our proposal discovers new events 30-40 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> faster than the other algorithms, and outperforms them in terms of percentage of visited events and inspection delay, under a wide variety of scenarios.